Online monitoring data processing methods for railway slopes and its application: A case study of the Shuohuang Railway by Mu GU

“…Engineering applications demonstrate that the proposed methods effectively detect and correct outliers, provide robust noise suppression, and yield precise deformation trend predictions, enhancing the practical application of monitoring systems.…”
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A Novel Multi-Step Forecasting-Based Approach for Enhanced Burst Detection in Water Distribution Systems by Xi Wan, Raziyeh Farmani, Edward Keedwell, Xiao Zhou

“…For an online burst detection method based on flow time series data, the challenge arises in the variability of anomaly definitions across different datasets, rendering a one-size-fits-all anomaly detection algorithm impossible. …”
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BLACKMAIL AS A METHOD OF EXTORTION by KHOMUTOVA RUSLANA, DAVTYAN DAVID

“…An important section is the analysis of the difficulties of detecting and investigating extortion, the features of latency, the use of new methods of criminology and interdepartmental interaction, as well as forecasting new threats. …”
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The usage of power system multi-model forecasting aided state estimation for cyber attack detection by I. A. Lukicheva, A. L. Kulikov

“…The aim of the research was to develop a state estimation algorithm, which is able to work in the presence of cyber-attack with high accuracy.METHODS. The authors propose a Multi-Model Forecasting-Aided State Estimation method based on multi-model discrete tracking parameter estimation by the Kalman filter. …”
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Trend Detection and Forecasting of LST in Tabriz City using the Non-parametric Mann-Kendall and NNAR by Mohammad Ali Koushesh Vatan, Akbar Asghari Zamani, Shahrivar Rostaei

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CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting by Josef Koumar, Karel Hynek, Tomáš Čejka, Pavel Šiška

“…Most approaches to anomaly detection use methods based on forecasting. Extensive real-world network datasets for forecasting and anomaly detection techniques are missing, potentially causing overestimation of anomaly detection algorithm performance and fabricating the illusion of progress. …”
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Geomorphological and Isotope-Geochemical Methods in Assessing the Gas Potential of a Territory (Using the Example of the Angara-Lena Oil and Gas Region) by Smirnov Aleksandr Sergeevich, Nourgaliev Danis, Chernova Inna Yur'evna, Ziganshin Eduard

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Marine soundscape forecasting: A deep learning-based approach by Shashidhar Siddagangaiah

“…Despite the rapid development of anomaly detection algorithms and deep-learning models for forecasting, their application to marine soundscapes remains unexplored. …”
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An integrated framework for multi-commodity agricultural price forecasting and anomaly detection using attention-boosted models by Eko Sediyono, Kristoko Dwi Hartomo, Christian Arthur, Intiyas Utami, Ronny Prabowo, Raymond Chiong

“…The proposed models outperformed baseline methods by achieving lower forecasting and anomaly detection errors. …”
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PREDICTION OF THE INCIDENCE OF PROSTATE CANCER IN THE URAL ECONOMIC REGION OF THE RUSSIAN FEDERATION by K. A. Ilyin, A. V. Zamyatin, G. P. Bystray, S. A. Okhotnikov

“…Objective. To quantify and to forecast the dynamics of registered cases of prostate cancer (PC) in the Ural economic region.Material and methods. …”
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Automatically Detected CSES Ionospheric Precursors Before Part of the Strong Aftershocks of the 23 January 2024 Wushi <i>M</i>_S 7.1 Earthquake in Northwest China by Mei Li, Hongzhu Yan, Tianyu Liu

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Analyzing Taiwanese Traffic Patterns on Consecutive Holidays Through Forecast Reconciliation and Prediction-Based Anomaly Detection Techniques by Mahsa Ashouri, Frederick Kin Hing Phoa, Marzia Angela Cremona

“…We propose a prediction-based detection method for identifying highway traffic anomalies using reconciled ordinary least squares (OLS) forecasts and bootstrap prediction intervals. …”
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Automated Detection of coronaL MAss Ejecta origiNs for Space Weather AppliCations (ALMANAC) by Thomas Williams, Huw Morgan

“…This paper presents a method that detects and estimates the central coordinates of CME eruptions in Extreme Ultraviolet data, with the dual aim of providing an early alert, and giving an initial estimate of the CME direction of propagation to a CME geometrical model. …”
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On the Method of Identification of Atypical Observations in Time Series by Maciej Oesterreich

“…The paper presents a method of detecting atypical observations in time series with or without seasonal fluctuations. …”
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Ensemble-based forecasting of wildfire potentials using relative index in Gangwon Province, South Korea by Sang Yeob Kim, Changhyun Jun, Wooyoung Na

“…This study proposes post-processing procedures for wildfire forecasting by applying statistical index-merging methods to enhance the utility of conventional wildfire indices in forested regions of South Korea. …”
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Integrated Time Series Analysis, Clustering, and Forecasting for Energy Efficiency Optimization and Tariff Management by Anderson Jhones Passos Nascimento, Menaouar Berrehil El Kattel, Jose Antonio Fernandes de Macedo, Fernando Luiz Marcelo Antunes

“…Additionally, the study incorporates consumption forecasting methods to estimate the behavior of new consumer units or those with limited historical data, optimizing the definition of contracted demand and promoting energy efficiency. …”
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Evaluating LSTM Performance on Multivariate Time Series with One-Class SVM Outlier Detection by Ragita Anillya Putri, Sugiyarto Surono, Aris Thobirin

“…Weekly sales forecasting plays a crucial role in retail business planning and inventory management.This study evaluates the prediction performance of a Long Short-Term Memory (LSTM) model for weekly sales forecasting after data preprocessing using standardization and outlier detection with One-Class Support Vector Machine (OCSVM) method. …”
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Forecasting the Detection of Lyman-alpha Forest Weak Lensing from the Dark Energy Spectroscopic Instrument and Other Future Surveys by Patrick Shaw, Rupert A. C. Croft, R. Benton Metcalf

“…We show that spectral surveys with low density and high volume are promising candidates for forest weak lensing in addition to the high resolution data that have been considered in previous work. We present forecasts for future spectral surveys and show that with larger datasets a detection with signal to noise $>10$ will be possible.…”
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Optimising energy distribution and detecting vulnerabilities in networks using artificial intelligence by D. Koshkin, O. Sadovoy, A. Rudenko, V. Sokolik

“…Load forecasting methods, including neural networks, decision trees, and reinforcement learning, contributed to reducing energy consumption and preventing overloads. …”
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Simulation Study to Identify Factors Affecting the Performance of LSTM and XGBoost for Anomaly Detection on Labeled Time Series Data by Muhammad Rizky Nurhambali, Yenni Angraini, Anwar Fitrianto

“…Both use a forecasting approach for anomaly detection. However, the limitations of both methods on anomalies, such as data length, labeling method, and number of anomalies have not been explored. …”
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